Coarse coding in natural and artificial vision systems

Coarse-coding is the transformation of raw data using a small number of broadly overlapping filters. These filters may exist in time, space, color, or other information domains. Inspired by models of natural vision processing, intensity and color information has been previously encoded and successfully decoded using coarse coding(1). The color and intensity of objects within test images were successfully retrieved after passing through only two coarse filters arranged in a checkerboard fashion. It was shown that a consequence of such a filter is a natural edge enhancement of the objects within the image. Coarse-coding is considered here in a signal processing frequency domain and in a sensory spectral filtering domain. Test signals include single frequency, multiple frequency, and signals with broad frequency content. Gaussian-based filters are used to discriminate between different signals of arbitrary frequency content The effects of Gaussian shape changes and spectral contrasting techniques are demonstrated. Consequences in filter parameter selection are further discussed.